COVID-19 RESPONSIVE DASHBOARD

"Ultimately, the greatest lesson that COVID-19 can teach humanity is that we are all in this together."-Kiran Mazumdar-Shaw.

Coronaviruses are a large family of viruses that cause illness ranging from the common cold to more severe diseases. A novel coronavirus is a new strain that has not been previously identified in humans. The new virus was subsequently named the “COVID-19 virus”.The coronavirus COVID-19 pandemic is the defining global health crisis of our time and the greatest challenge we have faced till now. Since its emergence in Asia in 2019, the virus has spread to every continent except Antarctica.

# libraries

from __future__ import print_function
from ipywidgets import interact, interactive, fixed, interact_manual
from IPython.core.display import display, HTML

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import plotly.express as px
import folium
import plotly.graph_objects as go
import seaborn as sns
import ipywidgets as widgets

# Data loading from github repository of jhon hopkins
death_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_deaths_global.csv')
confirmed_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_confirmed_global.csv')
recovered_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/master/csse_covid_19_data/csse_covid_19_time_series/time_series_covid19_recovered_global.csv')
country_df = pd.read_csv('https://raw.githubusercontent.com/CSSEGISandData/COVID-19/web-data/data/cases_country.csv')

# Data cleaning

# renaming the df column names to lowercase
country_df.columns = map(str.lower, country_df.columns)
confirmed_df.columns = map(str.lower, confirmed_df.columns)
death_df.columns = map(str.lower, death_df.columns)
recovered_df.columns = map(str.lower, recovered_df.columns)

# changing province/state to state and country/region to country
country_df = country_df.rename(columns={'country_region': 'country'})
country_df = country_df[['country','last_update','lat','long_','confirmed','deaths','recovered','active']]
confirmed_df = confirmed_df.rename(columns={'province/state': 'state', 'country/region': 'country'})
death_df = death_df.rename(columns={'province/state': 'state', 'country/region': 'country'})
recovered_df = confirmed_df.rename(columns={'province/state': 'state', 'country/region': 'country'})

# delete state as it contains nan value and it will cause problem while plotting map

# confirmed_df.drop('state', axis=1 ,inplace=True)
confirmed_df.lat = confirmed_df.lat.replace([np.inf, -np.inf], np.nan).dropna(axis=0)
confirmed_df.long = confirmed_df.long.replace([np.inf, -np.inf], np.nan).dropna(axis=0)
confirmed_df.lat=confirmed_df.lat.fillna(0)
confirmed_df.long=confirmed_df.long.fillna(0)

# Total number of confirmed, death and recovered cases
confirmed_total = int(country_df['confirmed'].sum())
deaths_total = int(country_df['deaths'].sum())
recovered_total = int(country_df['recovered'].sum())
active_total = int(country_df['active'].sum())

Total number of currently affected, dead and recovered patient worldwide.

# Displayt of Total cases:

display(HTML("<div style = 'background-color:#B6D0E2; padding: 30px '>" +
             "<span style='color: blue; font-size:30px;'> Confirmed: "  + str(confirmed_total) +"</span>" +
             "<span style='color: red; font-size:30px;margin-left:20px;'> Deaths: " + str(deaths_total) + "</span>"+
             "<span style='color: green; font-size:30px; margin-left:20px;'> Recovered: " + str(recovered_total) + "</span>"+
             "</div>")
       )

Confirmed: 171784138 Deaths: 3693798 Recovered: 109374115

Enter the number of countries you want data for:

fig = go.FigureWidget( layout=go.Layout() )
def highlight_col(x):
    r = 'background-color:  coral'
    y = 'background-color: lightblue'
    g = 'background-color: lightgreen'
    df1 = pd.DataFrame('', index=x.index, columns=x.columns)
    df1.iloc[:, 4] = y
    df1.iloc[:, 5] = r
    df1.iloc[:, 6] = g
    
    return df1

def show_latest_cases(Number):
    Number= int(Number)
    return country_df.sort_values('confirmed', ascending= False).head(Number).style.apply(highlight_col, axis=None)

interact(show_latest_cases, Number='10')

ipywLayout = widgets.Layout(border='solid 2px green')
ipywLayout.display='none' # uncomment this, run cell again - then the graph/figure disappears
widgets.VBox([fig], layout=ipywLayout)

10 Worst hit countries data:

Confirm cases

sorted_country_df = country_df.sort_values('confirmed', ascending= False).head(10)

px.bar(
    sorted_country_df.head(10),
    x = "country",
    y = "confirmed",
#     title= "Top 10 worst affected countries", # the axis names
    color_discrete_sequence=["Blue"], 
    height=500,
    width=600
)

Death cases

px.bar(
    sorted_country_df.head(10),
    x = "country",
    y = "deaths",
#     title= "Top 10 worst affected countries", # the axis names
    color_discrete_sequence=["red"], 
    height=500,
    width=600
)

Recovered cases

px.bar(
    sorted_country_df.head(10),
    x = "country",
    y = "recovered",
#     title= "Top 10 worst affected countries", # the axis names
    color_discrete_sequence=["green"], 
    height=500,
    width=600
)

FACT ABOUT COVID-19:

1. Covid-19 cannot be transmitted through mosquitoes.
2. Drinking alcohol, such as beer, liquor, wine doesn’t help prevent coronavirus
3. COVID-19 virus can spread in cold as well as hot climate. Virus has nothing to do with climate.
4. spraying alcohol or chlorine on your body donnot kill the virus inside.
5. Rinsing the nose with saline cannot protect against the coronavirus
6. The virus donnot die when temperatures rise

TOP 5 MOST AFFECTED COUNTRIES CONFIREMED CASSES

import plotly.express as px

  fig = px.scatter(sorted_country_df.head(5), x="country", y="confirmed", size="confirmed", color="country",
               hover_name="country", size_max=60)

 fig = px.scatter(sorted_country_df.head(5), x="country", y="confirmed", size="confirmed", color="country",
               hover_name="country", size_max=60)
#  update done according to us
 fig.update_layout(
    title=str(5) +" Worst hit countries",
    xaxis_title="Countries",
    yaxis_title="Confirmed Cases",
    width = 1000
    )
   
fig.show()

Confirmed v/s death case graph

Enter the country name of which you want information.(keep the first letter capital eg-India)

import plotly.graph_objects as go

def plot_cases_of_a_country(country):
    labels = ['confirmed', 'deaths']
    colors = ['blue', 'red']
    mode_size = [6, 8]
    line_size = [4, 5]
    
    df_list = [confirmed_df, death_df]
    
    fig = go.Figure();
    
    for i, df in enumerate(df_list):
        if country == 'World' or country == 'world':
            x_data = np.array(list(df.iloc[:, 5:].columns))
            y_data = np.sum(np.asarray(df.iloc[:,5:]),axis = 0)
            
        else:    
            x_data = np.array(list(df.iloc[:, 5:].columns))
            y_data = np.sum(np.asarray(df[df['country'] == country].iloc[:,20:]),axis = 0)
            
        fig.add_trace(go.Scatter(x=x_data, y=y_data, mode='lines+markers',
            name=labels[i],
            line=dict(color=colors[i], width=line_size[i]),
            connectgaps=True,
            text = "Total " + str(labels[i]) +": "+ str(y_data[-1])
             ))  
        
    fig.update_layout(
        title="COVID 19 cases of " + country,
        xaxis_title='Date',
        yaxis_title='No. of Confirmed Cases',
        margin=dict(l=20, r=20, t=40, b=20),
        paper_bgcolor="lightgrey",
        width = 800,
        
    )
        
        
    fig.show()
# plot_cases_of_a_country('US')
interact(plot_cases_of_a_country, country='World')
     
        

        |

FACTS ABOUT COVID-19 VACCINES(BUSTING THE MYTHS):

1. If one have already been infected with COVID-19 in the past, still they should be vaccinated.
2. Researchers did not rush the development of the COVID-19 vaccine, so it is effective as well as trustworthy.
3. Individuals who get the COVID-19 vaccination still need to practice infection prevention precautions and always Keep the mask on in public places
4. The side effects of the COVID-19 vaccine are not dangerous.

import folium

world_map = folium.Map(location=[20,0], tiles="Stamen Terrain", zoom_start=2)
confirmed_df.dropna(subset=['lat'],inplace=True)
confirmed_df.dropna(subset=['long'],inplace=True)

for i in range(0,len(confirmed_df)):
    folium.Marker( 
    location=[confirmed_df.iloc[i]['lat'], confirmed_df.iloc[i]['long']],
#     fill=True,
#     radius= (int((np.log(confirmed_df.iloc[i,-1]+1.00001))) + 0.2)*50000,
#     fill_color='white' ,
#     color='red',
    tooltip= "<div style='margin: 0; background-color: black; color: white;'>"+
                    "<h4 style='text-align:center;font-weight: bold'>"+confirmed_df.iloc[i]['country'] + "</h4>"
                    "<hr style='margin:10px;color: white;'>"+
                    "<ul style='color: white;;list-style-type:circle;align-item:left;padding-left:20px;padding-right:20px'>"+
                        "<li>Confirmed: "+str(confirmed_df.iloc[i,-1])+"</li>"+
                        "<li>Deaths:   "+str(death_df.iloc[i,-1])+"</li>"+
                        "<li>Death Rate: "+ str(np.round(death_df.iloc[i,-1]/(confirmed_df.iloc[i,-1]+1.00001)*100,2))+ "</li>"+
                    "</ul></div>",
    ).add_to(world_map)
world_map

SYMPTOMS:

Most common symptoms:

1. Fever
2. Dry cough
3. Tiredness

Less common symptoms:

1. Aches and pains
2. Sore throat
3. Diarrhoea
4. Conjunctivitis
5. Headache
6. Loss of taste or smell
7. A rash on skin, or discolouration of fingers or toe

PREVENTION TIPS:

1. Wear a mask or face covering in public places
2. Don’t go out if you’re feeling sick or have any cold or flu symptoms.
3. Avoid crowd or large gathering of people
4. Don’t touch your face, eyes, nose, or mouth when your hands are dirty.
5. Clean any objects you touch a lot. Use disinfectants on objects like phones, computers, and doorknobs etc.
6. Cover your mouth with a tissue or the inside of your elbow whenever you sneeze or cough. Throw away any tissues you use right away

TOGETHER WE CAN OVERCOME!

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